{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression with a Neural Network mindset\n",
    "\n",
    "Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize  cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.\n",
    "\n",
    "**Instructions:**\n",
    "- Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.\n",
    "\n",
    "**You will learn to:**\n",
    "- Build the general architecture of a learning algorithm, including:\n",
    "    - Initializing parameters\n",
    "    - Calculating the cost function and its gradient\n",
    "    - Using an optimization algorithm (gradient descent) \n",
    "- Gather all three functions above into a main model function, in the right order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "First, let's run the cell below to import all the packages that you will need during this assignment. \n",
    "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n",
    "- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.\n",
    "- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\n",
    "- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import scipy\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from lr_utils import load_dataset\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 2 - Overview of the Problem set ##\n",
    "\n",
    "**Problem Statement**: You are given a dataset (\"data.h5\") containing:\n",
    "    - a training set of m_train images labeled as cat (y=1) or non-cat (y=0)\n",
    "    - a test set of m_test images labeled as cat or non-cat\n",
    "    - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).\n",
    "\n",
    "You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.\n",
    "\n",
    "Let's get more familiar with the dataset. Load the data by running the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Loading the data (cat/non-cat)\n",
    "train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We added \"_orig\" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).\n",
    "\n",
    "Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the `index` value and re-run to see other images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = [1], it's a 'cat' picture.\n"
     ]
    },
    {
     "data": {
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U9YSy3qKcbvUpN2cFvTd34RMWLIwFAKhVkfX0irjrudDHxbEt6gTzH1QMRt+B3kUSoQKi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gAeuC3tT8zVQeaw8mrOqIO7fIiDKX9tRSu4bnL7NpA/K73S7UGwroSVEXZCUdl/6T6GeV8LZA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b400e2b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture\n",
    "index = 25\n",
    "plt.imshow(train_set_x_orig[index])\n",
    "print (\"y = \" + str(train_set_y[:, index]) + \", it's a '\" + classes[np.squeeze(train_set_y[:, index])].decode(\"utf-8\") +  \"' picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many software bugs in deep learning come from having matrix/vector dimensions that don't fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs. \n",
    "\n",
    "**Exercise:** Find the values for:\n",
    "    - m_train (number of training examples)\n",
    "    - m_test (number of test examples)\n",
    "    - num_px (= height = width of a training image)\n",
    "Remember that `train_set_x_orig` is a numpy-array of shape (m_train, num_px, num_px, 3). For instance, you can access `m_train` by writing `train_set_x_orig.shape[0]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: m_train = 209\n",
      "Number of testing examples: m_test = 50\n",
      "Height/Width of each image: num_px = 64\n",
      "Each image is of size: (64, 64, 3)\n",
      "train_set_x shape: (209, 64, 64, 3)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x shape: (50, 64, 64, 3)\n",
      "test_set_y shape: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "m_train = train_set_x_orig.shape[0]\n",
    "m_test = test_set_x_orig.shape[0]\n",
    "num_px = train_set_x_orig.shape[1]\n",
    "### END CODE HERE ###\n",
    "\n",
    "print (\"Number of training examples: m_train = \" + str(m_train))\n",
    "print (\"Number of testing examples: m_test = \" + str(m_test))\n",
    "print (\"Height/Width of each image: num_px = \" + str(num_px))\n",
    "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n",
    "print (\"train_set_x shape: \" + str(train_set_x_orig.shape))\n",
    "print (\"train_set_y shape: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x shape: \" + str(test_set_x_orig.shape))\n",
    "print (\"test_set_y shape: \" + str(test_set_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output for m_train, m_test and num_px**: \n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**m_train**</td>\n",
    "    <td> 209 </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**m_test**</td>\n",
    "    <td> 50 </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**num_px**</td>\n",
    "    <td> 64 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, you should now reshape images of shape (num_px, num_px, 3) in a numpy-array of shape (num_px $*$ num_px $*$ 3, 1). After this, our training (and test) dataset is a numpy-array where each column represents a flattened image. There should be m_train (respectively m_test) columns.\n",
    "\n",
    "**Exercise:** Reshape the training and test data sets so that images of size (num_px, num_px, 3) are flattened into single vectors of shape (num\\_px $*$ num\\_px $*$ 3, 1).\n",
    "\n",
    "A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b$*$c$*$d, a) is to use: \n",
    "```python\n",
    "X_flatten = X.reshape(X.shape[0], -1).T      # X.T is the transpose of X\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_set_x_flatten shape: (12288, 209)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x_flatten shape: (12288, 50)\n",
      "test_set_y shape: (1, 50)\n",
      "sanity check after reshaping: [17 31 56 22 33]\n"
     ]
    }
   ],
   "source": [
    "# Reshape the training and test examples\n",
    "\n",
    "### START CODE HERE ### (≈ 2 lines of code)\n",
    "train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0],-1).T\n",
    "test_set_x_flatten =  test_set_x_orig.reshape(test_set_x_orig.shape[0],-1).T\n",
    "### END CODE HERE ###\n",
    "\n",
    "print (\"train_set_x_flatten shape: \" + str(train_set_x_flatten.shape))\n",
    "print (\"train_set_y shape: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x_flatten shape: \" + str(test_set_x_flatten.shape))\n",
    "print (\"test_set_y shape: \" + str(test_set_y.shape))\n",
    "print (\"sanity check after reshaping: \" + str(train_set_x_flatten[0:5,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:35%\">\n",
    "  <tr>\n",
    "    <td>**train_set_x_flatten shape**</td>\n",
    "    <td> (12288, 209)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**train_set_y shape**</td>\n",
    "    <td>(1, 209)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**test_set_x_flatten shape**</td>\n",
    "    <td>(12288, 50)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**test_set_y shape**</td>\n",
    "    <td>(1, 50)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>**sanity check after reshaping**</td>\n",
    "  <td>[17 31 56 22 33]</td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To represent color images, the red, green and blue channels (RGB) must be specified for each pixel, and so the pixel value is actually a vector of three numbers ranging from 0 to 255.\n",
    "\n",
    "One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. But for picture datasets, it is simpler and more convenient and works almost as well to just divide every row of the dataset by 255 (the maximum value of a pixel channel).\n",
    "\n",
    "<!-- During the training of your model, you're going to multiply weights and add biases to some initial inputs in order to observe neuron activations. Then you backpropogate with the gradients to train the model. But, it is extremely important for each feature to have a similar range such that our gradients don't explode. You will see that more in detail later in the lectures. !--> \n",
    "\n",
    "Let's standardize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_set_x = train_set_x_flatten/255.\n",
    "test_set_x = test_set_x_flatten/255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you need to remember:**\n",
    "\n",
    "Common steps for pre-processing a new dataset are:\n",
    "- Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, ...)\n",
    "- Reshape the datasets such that each example is now a vector of size (num_px \\* num_px \\* 3, 1)\n",
    "- \"Standardize\" the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - General Architecture of the learning algorithm ##\n",
    "\n",
    "It's time to design a simple algorithm to distinguish cat images from non-cat images.\n",
    "\n",
    "You will build a Logistic Regression, using a Neural Network mindset. The following Figure explains why **Logistic Regression is actually a very simple Neural Network!**\n",
    "\n",
    "<img src=\"images/LogReg_kiank.png\" style=\"width:650px;height:400px;\">\n",
    "\n",
    "**Mathematical expression of the algorithm**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{(i)} = w^T x^{(i)} + b \\tag{1}$$\n",
    "$$\\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)})\\tag{2}$$ \n",
    "$$ \\mathcal{L}(a^{(i)}, y^{(i)}) =  - y^{(i)}  \\log(a^{(i)}) - (1-y^{(i)} )  \\log(1-a^{(i)})\\tag{3}$$\n",
    "\n",
    "The cost is then computed by summing over all training examples:\n",
    "$$ J = \\frac{1}{m} \\sum_{i=1}^m \\mathcal{L}(a^{(i)}, y^{(i)})\\tag{6}$$\n",
    "\n",
    "**Key steps**:\n",
    "In this exercise, you will carry out the following steps: \n",
    "    - Initialize the parameters of the model\n",
    "    - Learn the parameters for the model by minimizing the cost  \n",
    "    - Use the learned parameters to make predictions (on the test set)\n",
    "    - Analyse the results and conclude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Building the parts of our algorithm ## \n",
    "\n",
    "The main steps for building a Neural Network are:\n",
    "1. Define the model structure (such as number of input features) \n",
    "2. Initialize the model's parameters\n",
    "3. Loop:\n",
    "    - Calculate current loss (forward propagation)\n",
    "    - Calculate current gradient (backward propagation)\n",
    "    - Update parameters (gradient descent)\n",
    "\n",
    "You often build 1-3 separately and integrate them into one function we call `model()`.\n",
    "\n",
    "### 4.1 - Helper functions\n",
    "\n",
    "**Exercise**: Using your code from \"Python Basics\", implement `sigmoid()`. As you've seen in the figure above, you need to compute $sigmoid( w^T x + b) = \\frac{1}{1 + e^{-(w^T x + b)}}$ to make predictions. Use np.exp()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: sigmoid\n",
    "\n",
    "def sigmoid(z):\n",
    "    \"\"\"\n",
    "    Compute the sigmoid of z\n",
    "\n",
    "    Arguments:\n",
    "    z -- A scalar or numpy array of any size.\n",
    "\n",
    "    Return:\n",
    "    s -- sigmoid(z)\n",
    "    \"\"\"\n",
    "\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    s = 1/(1+np.exp(-z))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid([0, 2]) = [ 0.5         0.88079708]\n"
     ]
    }
   ],
   "source": [
    "print (\"sigmoid([0, 2]) = \" + str(sigmoid(np.array([0,2]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <td>**sigmoid([0, 2])**</td>\n",
    "    <td> [ 0.5         0.88079708]</td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initializing parameters\n",
    "\n",
    "**Exercise:** Implement parameter initialization in the cell below. You have to initialize w as a vector of zeros. If you don't know what numpy function to use, look up np.zeros() in the Numpy library's documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_with_zeros\n",
    "\n",
    "def initialize_with_zeros(dim):\n",
    "    \"\"\"\n",
    "    This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.\n",
    "    \n",
    "    Argument:\n",
    "    dim -- size of the w vector we want (or number of parameters in this case)\n",
    "    \n",
    "    Returns:\n",
    "    w -- initialized vector of shape (dim, 1)\n",
    "    b -- initialized scalar (corresponds to the bias)\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    w = np.zeros((dim,1))\n",
    "    b = 0.0\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(w.shape == (dim, 1))\n",
    "    assert(isinstance(b, float) or isinstance(b, int))\n",
    "    \n",
    "    return w, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [[ 0.]\n",
      " [ 0.]]\n",
      "b = 0.0\n"
     ]
    }
   ],
   "source": [
    "dim = 2\n",
    "w, b = initialize_with_zeros(dim)\n",
    "print (\"w = \" + str(w))\n",
    "print (\"b = \" + str(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:15%\">\n",
    "    <tr>\n",
    "        <td>  ** w **  </td>\n",
    "        <td> [[ 0.]\n",
    " [ 0.]] </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** b **  </td>\n",
    "        <td> 0 </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "For image inputs, w will be of shape (num_px $\\times$ num_px $\\times$ 3, 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - Forward and Backward propagation\n",
    "\n",
    "Now that your parameters are initialized, you can do the \"forward\" and \"backward\" propagation steps for learning the parameters.\n",
    "\n",
    "**Exercise:** Implement a function `propagate()` that computes the cost function and its gradient.\n",
    "\n",
    "**Hints**:\n",
    "\n",
    "Forward Propagation:\n",
    "- You get X\n",
    "- You compute $A = \\sigma(w^T X + b) = (a^{(0)}, a^{(1)}, ..., a^{(m-1)}, a^{(m)})$\n",
    "- You calculate the cost function: $J = -\\frac{1}{m}\\sum_{i=1}^{m}y^{(i)}\\log(a^{(i)})+(1-y^{(i)})\\log(1-a^{(i)})$\n",
    "\n",
    "Here are the two formulas you will be using: \n",
    "\n",
    "$$ \\frac{\\partial J}{\\partial w} = \\frac{1}{m}X(A-Y)^T\\tag{7}$$\n",
    "$$ \\frac{\\partial J}{\\partial b} = \\frac{1}{m} \\sum_{i=1}^m (a^{(i)}-y^{(i)})\\tag{8}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: propagate\n",
    "\n",
    "def propagate(w, b, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the cost function and its gradient for the propagation explained above\n",
    "\n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of size (num_px * num_px * 3, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)\n",
    "\n",
    "    Return:\n",
    "    cost -- negative log-likelihood cost for logistic regression\n",
    "    dw -- gradient of the loss with respect to w, thus same shape as w\n",
    "    db -- gradient of the loss with respect to b, thus same shape as b\n",
    "    \n",
    "    Tips:\n",
    "    - Write your code step by step for the propagation. np.log(), np.dot()\n",
    "    \"\"\"\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    # FORWARD PROPAGATION (FROM X TO COST)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A = sigmoid(np.dot(w.T,X)+b)                               # compute activation\n",
    "    cost = -1/m * np.sum(Y * np.log(A) + (1-Y) * (np.log(1-A)))  # compute cost\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # BACKWARD PROPAGATION (TO FIND GRAD)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    dz= (1/m)*(A - Y)\n",
    "    dw = np.dot(X,dz.T)\n",
    "    db = np.sum(dz)\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(dw.shape == w.shape)\n",
    "    assert(db.dtype == float)\n",
    "    cost = np.squeeze(cost)\n",
    "    assert(cost.shape == ())\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return grads, cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dw = [[ 0.99845601]\n",
      " [ 2.39507239]]\n",
      "db = 0.00145557813678\n",
      "cost = 5.80154531939\n"
     ]
    }
   ],
   "source": [
    "w, b, X, Y = np.array([[1.],[2.]]), 2., np.array([[1.,2.,-1.],[3.,4.,-3.2]]), np.array([[1,0,1]])\n",
    "grads, cost = propagate(w, b, X, Y)\n",
    "print (\"dw = \" + str(grads[\"dw\"]))\n",
    "print (\"db = \" + str(grads[\"db\"]))\n",
    "print (\"cost = \" + str(cost))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:50%\">\n",
    "    <tr>\n",
    "        <td>  ** dw **  </td>\n",
    "      <td> [[ 0.99845601]\n",
    "     [ 2.39507239]]</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** db **  </td>\n",
    "        <td> 0.00145557813678 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** cost **  </td>\n",
    "        <td> 5.801545319394553 </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### d) Optimization\n",
    "- You have initialized your parameters.\n",
    "- You are also able to compute a cost function and its gradient.\n",
    "- Now, you want to update the parameters using gradient descent.\n",
    "\n",
    "**Exercise:** Write down the optimization function. The goal is to learn $w$ and $b$ by minimizing the cost function $J$. For a parameter $\\theta$, the update rule is $ \\theta = \\theta - \\alpha \\text{ } d\\theta$, where $\\alpha$ is the learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: optimize\n",
    "\n",
    "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n",
    "    \"\"\"\n",
    "    This function optimizes w and b by running a gradient descent algorithm\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of shape (num_px * num_px * 3, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    print_cost -- True to print the loss every 100 steps\n",
    "    \n",
    "    Returns:\n",
    "    params -- dictionary containing the weights w and bias b\n",
    "    grads -- dictionary containing the gradients of the weights and bias with respect to the cost function\n",
    "    costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.\n",
    "    \n",
    "    Tips:\n",
    "    You basically need to write down two steps and iterate through them:\n",
    "        1) Calculate the cost and the gradient for the current parameters. Use propagate().\n",
    "        2) Update the parameters using gradient descent rule for w and b.\n",
    "    \"\"\"\n",
    "    \n",
    "    costs = []\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "        \n",
    "        \n",
    "        # Cost and gradient calculation (≈ 1-4 lines of code)\n",
    "        ### START CODE HERE ### \n",
    "        grads, cost = propagate(w, b, X, Y)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Retrieve derivatives from grads\n",
    "        dw = grads[\"dw\"]\n",
    "        db = grads[\"db\"]\n",
    "        \n",
    "        # update rule (≈ 2 lines of code)\n",
    "        ### START CODE HERE ###\n",
    "        w = w - (learning_rate*dw)\n",
    "        b = b - (learning_rate*db)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Record the costs\n",
    "        if i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "        \n",
    "        # Print the cost every 100 training examples\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    \n",
    "    params = {\"w\": w,\n",
    "              \"b\": b}\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return params, grads, costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [[ 0.19033591]\n",
      " [ 0.12259159]]\n",
      "b = 1.92535983008\n",
      "dw = [[ 0.67752042]\n",
      " [ 1.41625495]]\n",
      "db = 0.219194504541\n"
     ]
    }
   ],
   "source": [
    "params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)\n",
    "\n",
    "print (\"w = \" + str(params[\"w\"]))\n",
    "print (\"b = \" + str(params[\"b\"]))\n",
    "print (\"dw = \" + str(grads[\"dw\"]))\n",
    "print (\"db = \" + str(grads[\"db\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:40%\">\n",
    "    <tr>\n",
    "       <td> **w** </td>\n",
    "       <td>[[ 0.19033591]\n",
    " [ 0.12259159]] </td>\n",
    "    </tr>\n",
    "    \n",
    "    <tr>\n",
    "       <td> **b** </td>\n",
    "       <td> 1.92535983008 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "       <td> **dw** </td>\n",
    "       <td> [[ 0.67752042]\n",
    " [ 1.41625495]] </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "       <td> **db** </td>\n",
    "       <td> 0.219194504541 </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** The previous function will output the learned w and b. We are able to use w and b to predict the labels for a dataset X. Implement the `predict()` function. There is two steps to computing predictions:\n",
    "\n",
    "1. Calculate $\\hat{Y} = A = \\sigma(w^T X + b)$\n",
    "\n",
    "2. Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector `Y_prediction`. If you wish, you can use an `if`/`else` statement in a `for` loop (though there is also a way to vectorize this). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(w, b, X):\n",
    "    '''\n",
    "    Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of size (num_px * num_px * 3, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X\n",
    "    '''\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    Y_prediction = np.zeros((1,m))\n",
    "    w = w.reshape(X.shape[0], 1)\n",
    "    \n",
    "    # Compute vector \"A\" predicting the probabilities of a cat being present in the picture\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    A =  sigmoid(np.dot(w.T,X)+ b)\n",
    " \n",
    "    ### END CODE HERE ###  \n",
    "    for i in range(A.shape[1]):\n",
    "        \n",
    "        # Convert probabilities A[0,i] to actual predictions p[0,i]\n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "       ''''\n",
    "        x_exp = np.exp(A)\n",
    "        print(x_exp)\n",
    "        x_sum = np.sum(x_exp,axis=1,keepdims=True)\n",
    "        print(x_sum)\n",
    "        s = np.divide(x_exp,x_sum)\n",
    "        '''\n",
    "        \n",
    "    Y_prediction = 1. * (A > 0.5)\n",
    "        ### END CODE HERE ###\n",
    "    \n",
    "    assert(Y_prediction.shape == (1, m))\n",
    "    \n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions = [[ 1.  1.  0.]]\n"
     ]
    }
   ],
   "source": [
    "w = np.array([[0.1124579],[0.23106775]])\n",
    "b = -0.3\n",
    "X = np.array([[1.,-1.1,-3.2],[1.2,2.,0.1]])\n",
    "print (\"predictions = \" + str(predict(w, b, X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:30%\">\n",
    "    <tr>\n",
    "         <td>\n",
    "             **predictions**\n",
    "         </td>\n",
    "          <td>\n",
    "            [[ 1.  1.  0.]]\n",
    "         </td>  \n",
    "   </tr>\n",
    "\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<font color='blue'>\n",
    "**What to remember:**\n",
    "You've implemented several functions that:\n",
    "- Initialize (w,b)\n",
    "- Optimize the loss iteratively to learn parameters (w,b):\n",
    "    - computing the cost and its gradient \n",
    "    - updating the parameters using gradient descent\n",
    "- Use the learned (w,b) to predict the labels for a given set of examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Merge all functions into a model ##\n",
    "\n",
    "You will now see how the overall model is structured by putting together all the building blocks (functions implemented in the previous parts) together, in the right order.\n",
    "\n",
    "**Exercise:** Implement the model function. Use the following notation:\n",
    "    - Y_prediction for your predictions on the test set\n",
    "    - Y_prediction_train for your predictions on the train set\n",
    "    - w, costs, grads for the outputs of optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: model\n",
    "\n",
    "def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):\n",
    "    \"\"\"\n",
    "    Builds the logistic regression model by calling the function you've implemented previously\n",
    "    \n",
    "    Arguments:\n",
    "    X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)\n",
    "    Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)\n",
    "    X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)\n",
    "    Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)\n",
    "    num_iterations -- hyperparameter representing the number of iterations to optimize the parameters\n",
    "    learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()\n",
    "    print_cost -- Set to true to print the cost every 100 iterations\n",
    "    \n",
    "    Returns:\n",
    "    d -- dictionary containing information about the model.\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    \n",
    "    # initialize parameters with zeros (≈ 1 line of code)\n",
    "    w, b = initialize_with_zeros(X_train.shape[0])\n",
    "\n",
    "    # Gradient descent (≈ 1 line of code)\n",
    "    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost = False)\n",
    "    \n",
    "    # Retrieve parameters w and b from dictionary \"parameters\"\n",
    "    w = parameters[\"w\"]\n",
    "    b = parameters[\"b\"]\n",
    "    \n",
    "    # Predict test/train set examples (≈ 2 lines of code)\n",
    "    Y_prediction_test = predict(w,b,X_test)\n",
    "    Y_prediction_train = predict(w,b,X_train)\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Print train/test Errors\n",
    "    print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))\n",
    "    print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))\n",
    "\n",
    "    \n",
    "    d = {\"costs\": costs,\n",
    "         \"Y_prediction_test\": Y_prediction_test, \n",
    "         \"Y_prediction_train\" : Y_prediction_train, \n",
    "         \"w\" : w, \n",
    "         \"b\" : b,\n",
    "         \"learning_rate\" : learning_rate,\n",
    "         \"num_iterations\": num_iterations}\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to train your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy: 99.04306220095694 %\n",
      "test accuracy: 70.0 %\n"
     ]
    }
   ],
   "source": [
    "d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:40%\"> \n",
    "\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 0 **  </td> \n",
    "        <td> 0.693147 </td>\n",
    "    </tr>\n",
    "      <tr>\n",
    "        <td> <center> $\\vdots$ </center> </td> \n",
    "        <td> <center> $\\vdots$ </center> </td> \n",
    "    </tr>  \n",
    "    <tr>\n",
    "        <td> **Train Accuracy**  </td> \n",
    "        <td> 99.04306220095694 % </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>**Test Accuracy** </td> \n",
    "        <td> 70.0 % </td>\n",
    "    </tr>\n",
    "</table> \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Comment**: Training accuracy is close to 100%. This is a good sanity check: your model is working and has high enough capacity to fit the training data. Test error is 68%. It is actually not bad for this simple model, given the small dataset we used and that logistic regression is a linear classifier. But no worries, you'll build an even better classifier next week!\n",
    "\n",
    "Also, you see that the model is clearly overfitting the training data. Later in this specialization you will learn how to reduce overfitting, for example by using regularization. Using the code below (and changing the `index` variable) you can look at predictions on pictures of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:4: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 1, you predicted that it is a \"cat\" picture.\n"
     ]
    },
    {
     "data": {
      "image/png": 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zhgYDhm2nkwyY5vXTt9XVIXvsp/LeebSIvU/suKwX7JcV63o5gKgAwXijbFs1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cVkuoAxEOtnBWwdyCE96Vv018DUfkM0pqfUeWFj4uzXiAVFv5Ox8LdsFAdN2Nt1AZxiShgjZ69A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\n8FnOcpaz3KGcQfgsZznLWe5Q/hVcokhRgy2SdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b26b67f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture that was wrongly classified.\n",
    "index = 1\n",
    "plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))\n",
    "print (\"y = \" + str(test_set_y[0,index]) + \", you predicted that it is a \\\"\" + classes[d[\"Y_prediction_test\"][0,index]].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also plot the cost function and the gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4VENhIeyyizqZioiI1JWCRzUNGwYTJsDHH8ddiYiISP5S8KimY4+FDTcMfT1ERESkdhQ8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b26b676d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot learning curve (with costs)\n",
    "costs = np.squeeze(d['costs'])\n",
    "plt.plot(costs)\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations (per hundreds)')\n",
    "plt.title(\"Learning rate =\" + str(d[\"learning_rate\"]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Interpretation**:\n",
    "You can see the cost decreasing. It shows that the parameters are being learned. However, you see that you could train the model even more on the training set. Try to increase the number of iterations in the cell above and rerun the cells. You might see that the training set accuracy goes up, but the test set accuracy goes down. This is called overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 - Further analysis (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on building your first image classification model. Let's analyze it further, and examine possible choices for the learning rate $\\alpha$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choice of learning rate ####\n",
    "\n",
    "**Reminder**:\n",
    "In order for Gradient Descent to work you must choose the learning rate wisely. The learning rate $\\alpha$  determines how rapidly we update the parameters. If the learning rate is too large we may \"overshoot\" the optimal value. Similarly, if it is too small we will need too many iterations to converge to the best values. That's why it is crucial to use a well-tuned learning rate.\n",
    "\n",
    "Let's compare the learning curve of our model with several choices of learning rates. Run the cell below. This should take about 1 minute. Feel free also to try different values than the three we have initialized the `learning_rates` variable to contain, and see what happens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning rate is: 0.01\n",
      "train accuracy: 99.52153110047847 %\n",
      "test accuracy: 68.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "learning rate is: 0.001\n",
      "train accuracy: 88.99521531100478 %\n",
      "test accuracy: 64.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "learning rate is: 0.0001\n",
      "train accuracy: 68.42105263157895 %\n",
      "test accuracy: 36.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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BLL5rMd6e3qWfdM0as5vbmjVw9dVmOu5NNzk0gMyfDyNHwpkzUMIyA6IWs27K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\ngfJVTDdDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b26b67d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = [0.01, 0.001, 0.0001]\n",
    "models = {}\n",
    "for i in learning_rates:\n",
    "    print (\"learning rate is: \" + str(i))\n",
    "    models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)\n",
    "    print ('\\n' + \"-------------------------------------------------------\" + '\\n')\n",
    "\n",
    "for i in learning_rates:\n",
    "    plt.plot(np.squeeze(models[str(i)][\"costs\"]), label= str(models[str(i)][\"learning_rate\"]))\n",
    "\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations')\n",
    "\n",
    "legend = plt.legend(loc='upper center', shadow=True)\n",
    "frame = legend.get_frame()\n",
    "frame.set_facecolor('0.90')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: \n",
    "- Different learning rates give different costs and thus different predictions results.\n",
    "- If the learning rate is too large (0.01), the cost may oscillate up and down. It may even diverge (though in this example, using 0.01 still eventually ends up at a good value for the cost). \n",
    "- A lower cost doesn't mean a better model. You have to check if there is possibly overfitting. It happens when the training accuracy is a lot higher than the test accuracy.\n",
    "- In deep learning, we usually recommend that you: \n",
    "    - Choose the learning rate that better minimizes the cost function.\n",
    "    - If your model overfits, use other techniques to reduce overfitting. (We'll talk about this in later videos.) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 7 - Test with your own image (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Change your image's name in the following code\n",
    "    4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## START CODE HERE ## (PUT YOUR IMAGE NAME) \n",
    "my_image = \"my_image.jpg\"   # change this to the name of your image file \n",
    "## END CODE HERE ##\n",
    "\n",
    "# We preprocess the image to fit your algorithm.\n",
    "fname = \"images/\" + my_image\n",
    "image = np.array(ndimage.imread(fname, flatten=False))\n",
    "my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T\n",
    "my_predicted_image = predict(d[\"w\"], d[\"b\"], my_image)\n",
    "\n",
    "plt.imshow(image)\n",
    "print(\"y = \" + str(np.squeeze(my_predicted_image)) + \", your algorithm predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What to remember from this assignment:**\n",
    "1. Preprocessing the dataset is important.\n",
    "2. You implemented each function separately: initialize(), propagate(), optimize(). Then you built a model().\n",
    "3. Tuning the learning rate (which is an example of a \"hyperparameter\") can make a big difference to the algorithm. You will see more examples of this later in this course!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, if you'd like, we invite you to try different things on this Notebook. Make sure you submit before trying anything. Once you submit, things you can play with include:\n",
    "    - Play with the learning rate and the number of iterations\n",
    "    - Try different initialization methods and compare the results\n",
    "    - Test other preprocessings (center the data, or divide each row by its standard deviation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bibliography:\n",
    "- http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/\n",
    "- https://stats.stackexchange.com/questions/211436/why-do-we-normalize-images-by-subtracting-the-datasets-image-mean-and-not-the-c"
   ]
  }
 ],
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